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Estimation of failure probability using semi-definite logit model

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  • Hiroshi Konno
  • Naoya Kawadai
  • Dai Wu

Abstract

We will propose a new and practical method for estimating the failure probability of a large number of small to medium scale companies using their balance sheet data. We will use the maximum likelihood method to estimate the best parameters of the logit function, where the failure intensity function in its exponent is represented as a convex quadratic function instead of a commonly used linear function. The reasons for using this type of function are : (i) it can better represent the observed nonlinear dependence of failure probability on financial attributes, (ii) the resulting likelihood function can be maximized using a cutting plane algorithm developed for nonlinear semi-definite programming problems. We will show that we can achieve better prediction performance than the standard logit model, using thousands of sample companies. Copyright Springer-Verlag Berlin/Heidelberg 2003

Suggested Citation

  • Hiroshi Konno & Naoya Kawadai & Dai Wu, 2003. "Estimation of failure probability using semi-definite logit model," Computational Management Science, Springer, vol. 1(1), pages 59-73, December.
    • Handle: RePEc:spr:comgts:v:1:y:2003:i:1:p:59-73
    DOI: 10.1007/s10287-003-0001-6
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    Cited by:

    1. Katsuhiro Tanaka & Rei Yamamoto, 2023. "Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    2. Shun Arahata & Takayuki Okuno & Akiko Takeda, 2023. "Complexity analysis of interior-point methods for second-order stationary points of nonlinear semidefinite optimization problems," Computational Optimization and Applications, Springer, vol. 86(2), pages 555-598, November.
    3. R. Andreani & E. H. Fukuda & G. Haeser & D. O. Santos & L. D. Secchin, 2021. "On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming," Computational Optimization and Applications, Springer, vol. 79(3), pages 633-648, July.
    4. Ellen H. Fukuda & Bruno F. Lourenço, 2018. "Exact augmented Lagrangian functions for nonlinear semidefinite programming," Computational Optimization and Applications, Springer, vol. 71(2), pages 457-482, November.

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